Towards a Cloud Computing Paradigm for Big Data Analysis in Smart Cities

被引：35
|
作者：
Massobrio, R. [1 ]
Nesmachnow, S. [1 ]
Tchernykh, A. [2 ,3 ,4 ,6 ]
Avetisyan, A. [3 ,5 ,6 ]
Radchenko, G. [4 ]
机构：
[1] Univ Republica, Montevideo 11200, Uruguay
[2] CICESE Res Ctr, Carretera Tijuana Ensenada 3918, Ensenada 22860, Baja California, Mexico
[3] RAS, Inst Syst Programming, Moscow 109004, Russia
[4] South Ural State Univ, Chelyabinsk 454080, Russia
[5] Lomonosov Moscow State Univ, Moscow 119991, Russia
[6] Moscow Inst Phys & Technol, Dolgoprudnyi 141701, Moscow Oblast, Russia
来源：
PROGRAMMING AND COMPUTER SOFTWARE | 2018年 / 44卷 / 03期
关键词：
cloud computing; big data; smart cities; intelligent transportation systems; DESTINATION ESTIMATION;
D O I：
10.1134/S0361768818030052
中图分类号：
TP31 [计算机软件];
学科分类号：
081202 ; 0835 ;
摘要：
In this paper, we present a Big Data analysis paradigm related to smart cities using cloud computing infrastructures. The proposed architecture follows the MapReduce parallel model implemented using the Hadoop framework. We analyse two case studies: a quality-of-service assessment of public transportation system using historical bus location data, and a passenger-mobility estimation using ticket sales data from smartcards. Both case studies use real data from the transportation system of Montevideo, Uruguay. The experimental evaluation demonstrates that the proposed model allows processing large volumes of data efficiently.
引用
收藏
页码：181 / 189
页数：9
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